Understanding Air Transportation Market Dynamics using a Search Algorithm for Calibrating Travel Demand and Price

Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson

doi:10.2514/6.2015-2493

Cited by 3 publications

(2 citation statements)

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“…Necessary calculations are made for assigning individual agent characteristics and other behavior-driving factors. 2) Once the AIRLINE-EVOS system has been set up, a calibration loop-indicated by a dashed line-is started to match AIRLINE-EVOS simulated outcomes for airfare and demand to TSAM projections, as described in Kumar 6 . This calibration process implemented a search algorithm that iteratively adjusted on a market level, airfares and customer willingness-to-pay.…”

Section: B Ats-evos Process Flowmentioning

confidence: 99%

“…Thus the customer willingness to pay (WTP) attribute is integrally linked with the size of a market's demand. We then calibrate for each market, its WTP curve through a search algorithm described in Kumar 6 . In this way, we get close to accurately matching the level of market demand projected by TSAM scenarios through approximating customer decisions to not purchase a ticket if all of the offered airfares are more expensive than the maximum amount they are willing to pay.…”

Section: Customer Agents and State Variablesmentioning

confidence: 99%

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An Integrated Framework for Modeling Air Carrier Behavior, Policy, and Impacts in the U.S. Air Transportation System

Horio

Kumar

Stouffer

et al. 2015

AIAA Modeling and Simulation Technologies Conference

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The implementation of the Next Generation Air Transportation System (NextGen) in the United States is an ongoing challenge for policymakers due to the complexity of the air transportation system (ATS) with its broad array of stakeholders and dynamic interdependencies between them. The successful implementation of NextGen has a hard dependency on the active participation of U.S. commercial airlines. To assist policymakers in identifying potential policy designs that facilitate the implementation of NextGen, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework called the Air Transportation System Evolutionary Simulation (ATS-EVOS). This framework integrates large empirical data sets with multiple specialized models to simulate the evolution of the airline response to potential future policies and explore consequential impacts on ATS performance and market dynamics. In the ATS-EVOS configuration presented here, we leverage the Transportation Systems Analysis Model (TSAM), the Airline Evolutionary Simulation (AIRLINE-EVOS), the Airspace Concept Evaluation System (ACES), and the Aviation Environmental Design Tool (AEDT), all of which enable this research to comprehensively represent the complex facets of the ATS and its participants. We validated this baseline configuration of ATS-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments that explored potential implementations of a carbon tax, congestion pricing policy, and the dynamics for equipage of new technology by airlines. These experiments demonstrated ATS-EVOS's capabilities in responding to a wide range of potential NextGenrelated policies and utility for decision makers to gain insights for effective policy design.2 stakeholder-level decisions and behaviors have both a tactical and a strategic perspective, and they are influenced by socioeconomic, technological, and policy interactions.U.S. commercial airlines are a key stakeholder in the ATS and successful implementation of NextGen requires airlines to equip their fleets with enabling technologies, with the bulk (if not all) of the capital expense at their own cost. There are potentially significant installation costs involved and policies are necessary to best incentivize airlines to equip their fleets. The ATS-EVOS framework is from a stakeholder perspective and as such, leverages models developed in an agent-based context to more easily represent individual and organizational behaviors in response to proposed policies and other changes in the system 2 .Ultimately, we seek to provide a tool for decision makers to gain better insight into how the design of policies and incentives may help ensure system performance is balanced with stakeholder utilities, and reduces risk and uncertainty for achieving a more efficient, robust, and safer ATS. The focus on this paper is not on a specific model, but is in reference to the overall A...

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Section: B Ats-evos Process Flowmentioning

confidence: 99%

Section: Customer Agents and State Variablesmentioning

confidence: 99%